As urban populations grow, it\'s imperative to evaluate and enhance the quality of pedestrian paths from the user\'s perspective. Crowdedness, associated with discomfort and safety, is crucial in determining the overall walking quality and user experience. Previously utilized methods for measuring crowdedness, such as travel diaries and floating population surveys, were limited to collecting perceptual data from sporadic surveys with restricted spatial coverage. Similarly, methods based on CCTV or mobile service data have been used but present issues with blind spots and fail to consider pedestrian perspectives. Against this background, this study explores the feasibility of assessing crowdedness levels by measuring subjects\' physiological responses in a laboratory setting based on visual images of real and virtual environments. This study hypothesizes that the amount of people or vehicles passing by affects the electrodermal activity (EDA) of pedestrians, indicating the comfort level of using the environment. Experimental EDA data were measured using a wearable device while the subjects were watching videos showing different pedestrian traffic flows. Representative EDA signal features (e.g., skin conductance responses) were extracted after data pre-processing. Noticeable changes in EDA responses are observed when subjects countered specific environmental variations, such as differing volumes of passing people, on pedestrian paths. The findings suggest that EDA data can be instrumental in differentiating crowdedness levels on pedestrian paths. This study contributes to the body of knowledge by demonstrating the potential of EDA data to characterize the crowdedness experienced by pedestrians. This aids in the development of a novel, quantitative method to gauge pedestrian path crowdedness and to discern contributing factors, such as path width.

Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA.

Meta-analyses indicate differences in Pavlovian fear responses between anxious and non-anxious individuals using electrodermal activity (EDA). Recent research, however, has cast doubt on whether these effects are robust to different analytic choices. Using the multiverse approach conceived by Steegen et al. (2016), we surveyed analytic choices typically implemented in clinical fear conditioning research by conducting 1240 analyses reflecting different choice permutations. Only 1.45% of our analyses produced theoretically congruent statistically significant effects, and the strength and direction of the estimated effects varied substantially across EDA processing methods. We conclude that EDA-estimated fear learning differences are vulnerable to researcher degrees of freedom and make recommendations regarding which analytical choices should be approached with a high degree of caution.

The purpose of our study was to assess specific physiological parameters associated with stress responses in bladder cancer (BCa) patients compared to healthy individuals. By examining the transition from a supine to a sitting position, representing a mild physiological load, we investigated the changes in autonomic nervous system (ANS) activity as reflected by alterations in these parameters, indicating shifts in ANS regulation, using non-linear heart rate variability (HRV) parameters (0V%, 2UV%, parasympathetic and sympathetic nervous system - PNS and SNS indices), modified heart rate acceleration (ACmod) and deceleration capacities (DCmod), heart rate (HR), electrodermal activity (EDA), and also their correlations with perceived stress score. Our findings showed that BCa patients (n = 38) exhibited elevated resting HR, heightened SNS index, and increased EDA compared to their healthy counterparts (n = 47), indicating a notable physiological stress burden. The 0V% parameter showed a positive association with the SNS index, ACmod, HR, and EDA parameters, while displaying a negative correlation with the PNS index, DCmod and 2UV%. These non-linear HRV parameters, such as 0V% and 2UV%, offer nuanced insights into the complexities of heartbeat dynamics and autonomic regulation. After the transition from supine to sitting positions, BCa patients displayed higher EDA responses, indicating heightened stress reactivity and ANS sensitivity. These physiological distinctions persisted even when we did not prove differences in the levels of perceived stress between the studied groups. In conclusion, our study emphasizes the significance of identifying cancer patients at risk of ANS dysregulation, paving the way for tailored stress management strategies.

An unannounced balance loss during walking, i.e., balance perturbation, is a stressful event, which changes the activity of the sympathetic nervous system (SNS). We examined SNS response to unannounced balance perturbation during walking, simulating real-life condition of balance loss. We asked: do laboratory-induced unannounced balance losses during walking cause a sympathetic response, and-if so-does it habituate after a series of perturbations? Thirty-four young adults underwent a series of six successive unannounced balance perturbations while walking on a treadmill. Sympathetic activity was monitored continuously using electrodermal activity and compared before and immediately after each unannounced perturbation. All perturbations elicited a significant increase of electrodermal activity (P < 0.001), indicating a phasic increase in the sympathetic drive. The relative phasic increase of electrodermal activity caused by the first perturbation was significantly higher than the last perturbation (P < 0.05). Three different types of electrodermal activity behavior were observed: steady-level tonic SNS activity, increased SNS activity, and decreased SNS activity. Balance loss during walking triggers phasic SNS response, this response habituates after a series of unannounced balance perturbations. In addition, three distinct patterns of tonic sympathetic activity may imply variations in the ability of the SNS response to habituate across individuals.NEW & NOTEWORTHY Up to date, the literature typically provides information about sympathetic nervous system activity and relatively static balance. We believe that exposing participants to a balance loss during walking, i.e., unexpected perturbation, provides a more ecologically valid situation to measure sympathetic nervous system response; this provides new and vital knowledge that can have a significant impact and understanding of how the SNS responds to a loss of balance in a real-life situation.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals\' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored.
METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection.
RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD.
CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Anxiety is a common issue among university students, many of them experience anxiety, depression, and stress during their school life. This study aimed to compare the acute physiological stress responses of students divided into two groups according to their perceived anxiety levels (positive test anxiety, PTA+, and negative test anxiety, PTA-). Heart rate variability (HRV) and electrodermal activity (EDA) were used to assess stress.Thirty-one healthy volunteers participated in the study. Participants completed anxiety assessments, including the Westside Test Anxiety Scale (WTAS), the State-Trait Anxiety Inventory (STAI), and the Test State Anxiety Inventory (TSAI). Based on their scores, participants were categorized into PTA+ and PTA- groups. All participants underwent 24-h continuous recordings of pulse and electrodermal activity (EDA) on two separate occasions: one day prior to a written exam and during a designated exam-free day serving as a baseline control.We compared the HRV and EDA data obtained on a regular day and on an exam day between the two groups. Results showed that the PTA+ group had significantly higher heart rate, stress index, low frequency, and short-term detrended fluctuation analysis (DFAα1) on the exam day. The tonic EDA component was also higher in the PTA+ group. Stress-related HRV and EDA parameters were negatively correlated with exam scores.In conclusion, the study found that physiological stress indicators obtained from HRV and EDA are associated with perceived exam anxiety in students.

OBJECTIVE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.
METHODS: For this purpose, data related to the surgeon\'s ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.
RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons\' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).
CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon\'s health during robotic surgery.

Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA\'s performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different (p-value < 0.05 after Wilcoxon signed rank test with Bonferroni\'s correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli (p-value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.
METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.
RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.
CONCLUSIONS: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.